ORCID Profile
0000-0002-2202-202X
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Publisher: MDPI AG
Date: 10-03-2023
DOI: 10.3390/ELECTRONICS12061320
Abstract: The Internet of Things (IoT) has become increasingly popular, and opened new possibilities for applications in various domains. However, the IoT also poses security challenges due to the limited resources of the devices and its dynamic network topology. Routing attacks on 6LoWPAN-based IoT devices can be particularly challenging to detect because of its unique characteristics of the network. In recent years, several techniques have been proposed for detecting routing attacks, including anomaly detection. These techniques leverage different features of network traffic to identify and classify routing attacks. This paper focuses on routing attacks that target the Routing Protocol for Low-Power and Lossy Networks (RPL), which are widely used in 6LoWPAN-based IoT systems. The attacks discussed in this paper can be categorized as either inherited from Wireless Sensor Networks or exploiting vulnerabilities unique to RPL (known as RPL-specific attacks). The paper describes various RPL attacks, including Flood Attacks, Data-DoS/DDoS Attacks, Wormhole Attacks, RPL Rank Attacks, Blackhole Attacks, Version Attacks, and Sinkhole Attacks. In this paper, a novel Hybrid Intrusion Detection System (HIDS) that combines a decision tree classifier and a one-class Support Vector Machine classifier is proposed to detect routing attacks. The HIDS draws on the strengths of both a Signature Intrusion Detection System (SIDS) and an Anomaly-based Intrusion Detection System (AIDS) to identify routing attacks with a high degree of accuracy and a low false alarm rate. The routing dataset, which features genuine IoT network traffic and various kinds of routing attacks, was used to test the proposed HIDS. According to the findings, the hybrid IDS proposed in this study outperforms SIDS and AIDS approaches, with higher detection rates and lower false positive rates.
Publisher: MDPI AG
Date: 08-08-2023
DOI: 10.3390/ELECTRONICS12163382
Abstract: Detecting anomalies, intrusions, and security threats in the network (including Internet of Things) traffic necessitates the processing of large volumes of sensitive data, which raises concerns about privacy and security. Federated learning, a distributed machine learning approach, enables multiple parties to collaboratively train a shared model while preserving data decentralization and privacy. In a federated learning environment, instead of training and evaluating the model on a single machine, each client learns a local model with the same structure but is trained on different local datasets. These local models are then communicated to an aggregation server that employs federated averaging to aggregate them and produce an optimized global model. This approach offers significant benefits for developing efficient and effective intrusion detection system (IDS) solutions. In this research, we investigated the effectiveness of federated learning for IDSs and compared it with that of traditional deep learning models. Our findings demonstrate that federated learning, by utilizing random client selection, achieved higher accuracy and lower loss compared to deep learning, particularly in scenarios emphasizing data privacy and security. Our experiments highlight the capability of federated learning to create global models without sharing sensitive data, thereby mitigating the risks associated with data breaches or leakage. The results suggest that federated averaging in federated learning has the potential to revolutionize the development of IDS solutions, thus making them more secure, efficient, and effective.
Publisher: IntechOpen
Date: 27-02-2023
DOI: 10.5772/INTECHOPEN.109840
Abstract: Recently, the exponential growth of Internet of Things (IoT) network-connected devices has resulted in the exchange of large amounts of data via a smart grid. This extensive connection between IoT devices results in numerous security breaches and violations. Due to the increasing prevalence of IoT-related cybercrimes, forensic investigators and researchers face numerous obstacles when attempting to recover evidence from a variety of different types of IoT smart devices. The primary challenge in performing forensic analysis on the IoT is the heterogeneity of IoT devices. Additionally, the bulk of IoT devices has flash memory or limited memory, which makes generating and converting evidence for presenting forensic data in court problematic. This review paper presents several forensic methodologies, techniques, and challenges in IoT device forensics, a comprehensive review of prominent recent works, with an overview of tools that are frequently used for performing digital forensics investigations. Additionally, a comparative analysis of three popular digital forensic tools is also conducted.
Publisher: MDPI AG
Date: 22-07-2022
DOI: 10.3390/FI14080217
Abstract: Websites on the Internet are becoming increasingly vulnerable to malicious JavaScript code because of its strong impact and dramatic effect. Numerous recent cyberattacks use JavaScript vulnerabilities, and in some cases employ obfuscation to conceal their malice and elude detection. To secure Internet users, an adequate intrusion-detection system (IDS) for malicious JavaScript must be developed. This paper proposes an automatic IDS of obfuscated JavaScript that employs several features and machine-learning techniques that effectively distinguish malicious and benign JavaScript codes. We also present a new set of features, which can detect obfuscation in JavaScript. The features are selected based on identifying obfuscation, a popular method to bypass conventional malware detection systems. The performance of the suggested approach has been tested on JavaScript obfuscation attacks. The studies have shown that IDS based on selected features has a detection rate of 94% for malicious s les and 81% for benign s les within the dimension of the feature vector of 60.
No related grants have been discovered for Sarabjot Singh.